Effects of spatial aggregation on predictions of forest climate change response

Abstract

We investigated the influence of spatial aggregation on modeled forest responses to climate change by applying the process-based Terrestrial Ecosystem Model (TEM) to a fine resolution spatial grid (100 km 2 ) and to a coarse resolution spatial grid (2500 km 2 ). Three climate scenarios were simulated: baseline (present) climate with ambient CO 2 and 2 future climates derived from the general circulation models OSU and GFDL-Q with elevated atmospheric CO 2 . For baseline climate, the aggregation error of the national (U.S.) study area was very small, -0.4%. Forest-level aggregation error ranged from -1.6 to 11.8%, with the largest aggregation error occurring in boreal forest types. Coarse grid resolution inputs underestimated production for boreal and forested boreal wetland forests and overestimated net primary production (NPP) for temperate conifer, temperate deciduous, and temperate forested wetland forests. Aggregation error for coarse grid cells ranged between -25.6 and 27.3%. Aggregation errors were especially large in transition regions between temperate and boreal forest types. An analysis that homogenized inputs for the 10 km grid cells within a 50 km grid indicated that aggregation of forest types and air temperature from fine to coarse grid cells contributed most to the spatial aggregation error. The aggregation error for the OSU climate was similar to the GFDL-Q climate and both results were similar to the aggregation error of the baseline climate in magnitude, sign, and spatial pattern. While aggregation error was similar across the baseline, GFDL-Q and OSU scenarios, NPP response to the GFDL-Q and OSU climates increased 13 to 30% above the baseline NPP. Within each climate scenario, the estimated NPP response to climate change differed by less than 1% between the coarse and fine resolutions. Except for transition regions and regions with substantial variability in air temperature, our simulations indicate that the use of 0.5°resolution provides an acceptable level of aggregation error at the 3 scales of analysis in this study. Improvements could be made by focusing computational intensity in heterogeneous regions and avoid computational intensity in regions that are relatively homogeneous with respect to vegetation and air temperature.

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

“…The findings of the overall aggregation effect on NPP agrees with the findings of Nungesser et al (1999), who found a mean uncertainty of <2% on NPP by modifying precipitation and solar radiation for resolutions of 10 and 50 km. The average differences for the wheat NN approach in this study (1.2%) is also in this range, but includes a large increase for AgroC of up to 3.3%.…”

“…In both studies, the impact was minor for averages over the entire study area, but showed impacts for smaller areas, especially for areas dominated by strong relief changes (Pierce and Running, 1995). In both studies, the effects were tested by one model and for two resolutions of 10 and 50 km grid cells in Nungesser et al (1999), and 1 km and 110 km in Pierce and Running (1995). The latter study investigated the effect for different input variables (relief, climate and soil) and found that climate data aggregation was the dominant variable affecting scale differences of NPP.…”

“…van Bussel et al (2011) investigated the impacts of climate aggregation on croplands and focused on phenological stages rather than primary production, but they also found minor effects on simulated average values. The impacts of climate data aggregation on NPP were tested by Nungesser et al (1999) and Pierce and Running (1995), for American forests. In both studies, the impact was minor for averages over the entire study area, but showed impacts for smaller areas, especially for areas dominated by strong relief changes (Pierce and Running, 1995).…”

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

“…Although the relevance of scale (Hansen & Jones 2000, Ewert et al 2011, Nendel et al 2013 and spatial data aggregation (Gardner et al 1982, Cale et al 1983, Cale & O'Neill 1988, Rastetter et al 1992, Pierce & Running 1995, Nungesser et al 1999, Gong et al 2003, Syphard & Franklin 2004, Lorite et al 2005, Ershadi et al 2013 is well known and data aggre gation has been addressed, for instance, in soil or hydrological process modelling (Heuvelink & Pebesma 1999, Haverkamp et al 2005, Leopold et al 2006, Bormann et al 2009, few studies have characterized the effect in application of crop models with spatially aggregated climate input data on simulated regional yields, hereafter called the aggregation effect. For example, De Wit et al (2005) used precipitation and radiation aggregated from 10 to 50 km resolution as model input to simulate winter wheat and grain maize yields in Germany and France.…”

Modelling carbon responses of tundra ecosystems to historical and projected climate: a comparison of a plot‐ and a global‐scale ecosystem model to identify process‐based uncertainties

“…Differences in the representation of ecosystem processes by ecosystem models may be caused by the following: (1) different conceptualizations of the relationships among ecosystem processes, different formulations of the same ecosystem processes, and different parameterizations of the same formulations Cramer et al 1999;Jenkins et al 1999); (2) the use of different input data sets (Pan et al 1996); (3) application of the models at different spatial resolutions (Jenkins et al 1999;Nungesser et al 1999); or (4) the use of different simulation protocols in applying the models. To identify process-based uncertainties related to differences in conceptualization, formulation, and parameterization among models, it is necessary to minimize differences associated with other sources by standardizing input data sets and simulation protocols, which is a standard practice in formal comparisons among models VEMAP Members 1995;Heimann et al 1998;Cramer et al 1999;Jenkins et al 1999;Kicklighter et al 1999;Schimel et al 2000).…”